Much of the difficulty in rendering consistent evaluation of expression patterns in cancer tissue microarrays arises from subjective impressions of observers. The literature shows that when characterizations are based upon computer-aided analysis, objectivity, reproducibility and sensitivity improve considerably. Advanced imaging and computational tools could potentially enable investigators to detect and track subtle changes in measurable parameters leading to the discovery of novel diagnostic and prognostic clues which are not apparent by human visual inspection alone. The central objective of this proposal is to design, develop, deploy and evaluate a content-based image retrieval system for performing quick, reliable comparative analysis of expression patterns in cancer tissue microarrays. The proposed project is a natural extension of the lead investigators' prior work and leverages several key resources and computational tools including a web-based image guided decision support system, a distributed telemicroscopy system, a virtual microscopy system, DataCutter which allows distributed execution of algorithms on computer and storage clusters, an intelligent image archival system, and Grid middleware components from the cancer Biomedical Informatics Grid (caBIGTM) In-vivo Imaging Workspace. The distinguishing characteristics of the proposed project are the capacity to support queries and perform comparisons across large datasets originating from both standard robotic and virtual microscopes and the capacity to automatically locate and retrieve those imaged tissue discs from within distributed, "gold-standard" archives which exhibit expression patterns which are most similar to those of a given query disc. Based upon the majority logic of the ranked retrievals query discs will be objectively and reproducibly assessed and classified. To test these technologies a multi-institutional, Grid-enabled laboratory will be established among strategic sites located at The Cancer Institute of New Jersey (CINJ), Columbia University (CU), the Ohio State University (OSU), Rutgers University (RU), the University of Medicine & Dentistry of New Jersey (UMDNJ), and the University of Pennsylvania School of Medicine (UPenn). This laboratory will be built using the caBIG caGrid infrastructure. Upon completion of the project, the software and underlying technologies will be made available to the scientific community as caBIG compliant resources for collaborative research, education and clinical decision support.